pytorch花朵分类代码，自定义卷积神经网络

好的，以下是一个使用PyTorch实现花朵分类的自定义卷积神经网络的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms

# 定义数据预处理
transform = transforms.Compose([
    transforms.Resize(224),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# 加载数据
train_dataset = datasets.ImageFolder('./data/train', transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)
test_dataset = datasets.ImageFolder('./data/test', transform=transform)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=32, shuffle=False)

# 定义模型
class FlowerNet(nn.Module):
    def __init__(self):
        super(FlowerNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm2d(32)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm2d(64)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.bn3 = nn.BatchNorm2d(128)
        self.conv4 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.bn4 = nn.BatchNorm2d(256)
        self.fc1 = nn.Linear(256 * 7 * 7, 1024)
        self.fc2 = nn.Linear(1024, 5)
        self.dropout = nn.Dropout(0.5)
    
    def forward(self, x):
        x = nn.functional.relu(self.bn1(self.conv1(x)))
        x = nn.functional.max_pool2d(x, 2)
        x = nn.functional.relu(self.bn2(self.conv2(x)))
        x = nn.functional.max_pool2d(x, 2)
        x = nn.functional.relu(self.bn3(self.conv3(x)))
        x = nn.functional.max_pool2d(x, 2)
        x = nn.functional.relu(self.bn4(self.conv4(x)))
        x = nn.functional.max_pool2d(x, 2)
        x = x.view(-1, 256 * 7 * 7)
        x = nn.functional.relu(self.fc1(x))
        x = self.dropout(x)
        x = self.fc2(x)
        return x

model = FlowerNet()

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
for epoch in range(10):
    running_loss = 0.0
    for i, (inputs, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 20 == 19:
            print('[%d, %d] loss: %.3f' % (epoch+1, i+1, running_loss/20))
            running_loss = 0.0

# 在测试集上测试模型
correct = 0
total = 0
with torch.no_grad():
    for inputs, labels in test_loader:
        outputs = model(inputs)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy on test set: %d %%' % (100 * correct / total))

这个示例代码中，我们定义了一个名为`FlowerNet`的类，继承自`nn.Module`，并在其中定义了一个包含卷积层、批量归一化层、线性层和Dropout层的卷积神经网络。我们使用`nn.functional`中的函数来定义网络的前向传播过程。在训练过程中，我们使用交叉熵损失函数和Adam优化器来进行模型的优化。在测试过程中，我们使用测试集来测试模型的准确率。